Overview

Dataset statistics

Number of variables23
Number of observations129880
Missing cells393
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.8 MiB
Average record size in memory184.0 B

Variable types

Categorical6
Numeric17

Alerts

Seat comfort is highly overall correlated with Food and drinkHigh correlation
Departure/Arrival time convenient is highly overall correlated with Food and drink and 1 other fieldsHigh correlation
Food and drink is highly overall correlated with Seat comfort and 2 other fieldsHigh correlation
Gate location is highly overall correlated with Departure/Arrival time convenient and 1 other fieldsHigh correlation
Inflight wifi service is highly overall correlated with Online support and 2 other fieldsHigh correlation
Inflight entertainment is highly overall correlated with satisfactionHigh correlation
Online support is highly overall correlated with Inflight wifi service and 2 other fieldsHigh correlation
Ease of Online booking is highly overall correlated with Inflight wifi service and 2 other fieldsHigh correlation
On-board service is highly overall correlated with CleanlinessHigh correlation
Cleanliness is highly overall correlated with On-board serviceHigh correlation
Online boarding is highly overall correlated with Inflight wifi service and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly overall correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly overall correlated with Departure Delay in MinutesHigh correlation
satisfaction is highly overall correlated with Inflight entertainmentHigh correlation
Type of Travel is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with Type of TravelHigh correlation
Seat comfort has 4797 (3.7%) zerosZeros
Departure/Arrival time convenient has 6664 (5.1%) zerosZeros
Food and drink has 5945 (4.6%) zerosZeros
Inflight entertainment has 2978 (2.3%) zerosZeros
Departure Delay in Minutes has 73356 (56.5%) zerosZeros
Arrival Delay in Minutes has 72753 (56.0%) zerosZeros

Reproduction

Analysis started2023-07-25 10:16:59.467198
Analysis finished2023-07-25 10:17:46.171716
Duration46.7 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

satisfaction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
satisfied
71087 
dissatisfied
58793 

Length

Max length12
Median length9
Mean length10.358015
Min length9

Characters and Unicode

Total characters1345299
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsatisfied
2nd rowsatisfied
3rd rowsatisfied
4th rowsatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
satisfied 71087
54.7%
dissatisfied 58793
45.3%

Length

2023-07-25T11:17:46.273728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T11:17:46.409122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
satisfied 71087
54.7%
dissatisfied 58793
45.3%

Most occurring characters

ValueCountFrequency (%)
s 318553
23.7%
i 318553
23.7%
d 188673
14.0%
a 129880
9.7%
t 129880
9.7%
f 129880
9.7%
e 129880
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1345299
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 318553
23.7%
i 318553
23.7%
d 188673
14.0%
a 129880
9.7%
t 129880
9.7%
f 129880
9.7%
e 129880
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1345299
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 318553
23.7%
i 318553
23.7%
d 188673
14.0%
a 129880
9.7%
t 129880
9.7%
f 129880
9.7%
e 129880
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1345299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 318553
23.7%
i 318553
23.7%
d 188673
14.0%
a 129880
9.7%
t 129880
9.7%
f 129880
9.7%
e 129880
9.7%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Female
65899 
Male
63981 

Length

Max length6
Median length6
Mean length5.0147675
Min length4

Characters and Unicode

Total characters651318
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 65899
50.7%
Male 63981
49.3%

Length

2023-07-25T11:17:46.520740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T11:17:46.651735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 65899
50.7%
male 63981
49.3%

Most occurring characters

ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 521438
80.1%
Uppercase Letter 129880
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 195779
37.5%
a 129880
24.9%
l 129880
24.9%
m 65899
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 65899
50.7%
M 63981
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 651318
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 651318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Loyal Customer
106100 
disloyal Customer
23780 

Length

Max length17
Median length14
Mean length14.549276
Min length14

Characters and Unicode

Total characters1889660
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowLoyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer 106100
81.7%
disloyal Customer 23780
 
18.3%

Length

2023-07-25T11:17:46.752980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T11:17:46.872998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
customer 129880
50.0%
loyal 106100
40.8%
disloyal 23780
 
9.2%

Most occurring characters

ValueCountFrequency (%)
o 259760
13.7%
l 153660
 
8.1%
s 153660
 
8.1%
y 129880
 
6.9%
a 129880
 
6.9%
129880
 
6.9%
C 129880
 
6.9%
u 129880
 
6.9%
t 129880
 
6.9%
m 129880
 
6.9%
Other values (5) 413420
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1523800
80.6%
Uppercase Letter 235980
 
12.5%
Space Separator 129880
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 259760
17.0%
l 153660
10.1%
s 153660
10.1%
y 129880
8.5%
a 129880
8.5%
u 129880
8.5%
t 129880
8.5%
m 129880
8.5%
e 129880
8.5%
r 129880
8.5%
Other values (2) 47560
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 129880
55.0%
L 106100
45.0%
Space Separator
ValueCountFrequency (%)
129880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1759780
93.1%
Common 129880
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 259760
14.8%
l 153660
8.7%
s 153660
8.7%
y 129880
7.4%
a 129880
7.4%
C 129880
7.4%
u 129880
7.4%
t 129880
7.4%
m 129880
7.4%
e 129880
7.4%
Other values (4) 283540
16.1%
Common
ValueCountFrequency (%)
129880
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1889660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 259760
13.7%
l 153660
 
8.1%
s 153660
 
8.1%
y 129880
 
6.9%
a 129880
 
6.9%
129880
 
6.9%
C 129880
 
6.9%
u 129880
 
6.9%
t 129880
 
6.9%
m 129880
 
6.9%
Other values (5) 413420
21.9%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.427957
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:46.991305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.11936
Coefficient of variation (CV)0.38346801
Kurtosis-0.71914023
Mean39.427957
Median Absolute Deviation (MAD)12
Skewness-0.0036062117
Sum5120903
Variance228.59505
MonotonicityNot monotonic
2023-07-25T11:17:47.130094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 3692
 
2.8%
25 3511
 
2.7%
40 3209
 
2.5%
44 3104
 
2.4%
41 3089
 
2.4%
42 3017
 
2.3%
43 2941
 
2.3%
45 2939
 
2.3%
23 2935
 
2.3%
22 2931
 
2.3%
Other values (65) 98512
75.8%
ValueCountFrequency (%)
7 685
0.5%
8 797
0.6%
9 859
0.7%
10 822
0.6%
11 837
0.6%
12 794
0.6%
13 806
0.6%
14 860
0.7%
15 1006
0.8%
16 1156
0.9%
ValueCountFrequency (%)
85 25
 
< 0.1%
80 110
0.1%
79 52
 
< 0.1%
78 44
 
< 0.1%
77 106
0.1%
76 60
 
< 0.1%
75 76
 
0.1%
74 61
 
< 0.1%
73 67
 
0.1%
72 249
0.2%

Type of Travel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Business travel
89693 
Personal Travel
40187 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1948200
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowPersonal Travel
3rd rowPersonal Travel
4th rowPersonal Travel
5th rowPersonal Travel

Common Values

ValueCountFrequency (%)
Business travel 89693
69.1%
Personal Travel 40187
30.9%

Length

2023-07-25T11:17:47.265463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T11:17:47.424494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
travel 129880
50.0%
business 89693
34.5%
personal 40187
 
15.5%

Most occurring characters

ValueCountFrequency (%)
s 309266
15.9%
e 259760
13.3%
r 170067
8.7%
a 170067
8.7%
l 170067
8.7%
n 129880
6.7%
129880
6.7%
v 129880
6.7%
B 89693
 
4.6%
u 89693
 
4.6%
Other values (5) 299947
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1648253
84.6%
Uppercase Letter 170067
 
8.7%
Space Separator 129880
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 309266
18.8%
e 259760
15.8%
r 170067
10.3%
a 170067
10.3%
l 170067
10.3%
n 129880
7.9%
v 129880
7.9%
u 89693
 
5.4%
i 89693
 
5.4%
t 89693
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 89693
52.7%
P 40187
23.6%
T 40187
23.6%
Space Separator
ValueCountFrequency (%)
129880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1818320
93.3%
Common 129880
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 309266
17.0%
e 259760
14.3%
r 170067
9.4%
a 170067
9.4%
l 170067
9.4%
n 129880
7.1%
v 129880
7.1%
B 89693
 
4.9%
u 89693
 
4.9%
i 89693
 
4.9%
Other values (4) 210254
11.6%
Common
ValueCountFrequency (%)
129880
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1948200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 309266
15.9%
e 259760
13.3%
r 170067
8.7%
a 170067
8.7%
l 170067
8.7%
n 129880
6.7%
129880
6.7%
v 129880
6.7%
B 89693
 
4.6%
u 89693
 
4.6%
Other values (5) 299947
15.4%

Class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Business
62160 
Eco
58309 
Eco Plus
9411 

Length

Max length8
Median length8
Mean length5.7552741
Min length3

Characters and Unicode

Total characters747495
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco
2nd rowBusiness
3rd rowEco
4th rowEco
5th rowEco

Common Values

ValueCountFrequency (%)
Business 62160
47.9%
Eco 58309
44.9%
Eco Plus 9411
 
7.2%

Length

2023-07-25T11:17:47.532949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T11:17:47.664727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
eco 67720
48.6%
business 62160
44.6%
plus 9411
 
6.8%

Most occurring characters

ValueCountFrequency (%)
s 195891
26.2%
u 71571
 
9.6%
E 67720
 
9.1%
c 67720
 
9.1%
o 67720
 
9.1%
B 62160
 
8.3%
i 62160
 
8.3%
n 62160
 
8.3%
e 62160
 
8.3%
9411
 
1.3%
Other values (2) 18822
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 598793
80.1%
Uppercase Letter 139291
 
18.6%
Space Separator 9411
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 195891
32.7%
u 71571
 
12.0%
c 67720
 
11.3%
o 67720
 
11.3%
i 62160
 
10.4%
n 62160
 
10.4%
e 62160
 
10.4%
l 9411
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E 67720
48.6%
B 62160
44.6%
P 9411
 
6.8%
Space Separator
ValueCountFrequency (%)
9411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 738084
98.7%
Common 9411
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 195891
26.5%
u 71571
 
9.7%
E 67720
 
9.2%
c 67720
 
9.2%
o 67720
 
9.2%
B 62160
 
8.4%
i 62160
 
8.4%
n 62160
 
8.4%
e 62160
 
8.4%
P 9411
 
1.3%
Common
ValueCountFrequency (%)
9411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 195891
26.2%
u 71571
 
9.6%
E 67720
 
9.1%
c 67720
 
9.1%
o 67720
 
9.1%
B 62160
 
8.3%
i 62160
 
8.3%
n 62160
 
8.3%
e 62160
 
8.3%
9411
 
1.3%
Other values (2) 18822
 
2.5%

Flight Distance
Real number (ℝ)

Distinct5398
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1981.4091
Minimum50
Maximum6951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:47.787141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile341
Q11359
median1925
Q32544
95-th percentile3831
Maximum6951
Range6901
Interquartile range (IQR)1185

Descriptive statistics

Standard deviation1027.1156
Coefficient of variation (CV)0.51837636
Kurtosis0.36430599
Mean1981.4091
Median Absolute Deviation (MAD)594
Skewness0.46674752
Sum2.5734541 × 108
Variance1054966.5
MonotonicityNot monotonic
2023-07-25T11:17:47.927887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1963 92
 
0.1%
1812 88
 
0.1%
1639 87
 
0.1%
1981 86
 
0.1%
1789 86
 
0.1%
1766 83
 
0.1%
1759 83
 
0.1%
1748 82
 
0.1%
2022 81
 
0.1%
1769 81
 
0.1%
Other values (5388) 129031
99.3%
ValueCountFrequency (%)
50 23
< 0.1%
51 21
< 0.1%
52 21
< 0.1%
53 28
< 0.1%
54 21
< 0.1%
55 22
< 0.1%
56 30
< 0.1%
57 21
< 0.1%
58 15
< 0.1%
59 24
< 0.1%
ValueCountFrequency (%)
6951 1
< 0.1%
6950 1
< 0.1%
6948 1
< 0.1%
6924 1
< 0.1%
6907 2
< 0.1%
6889 1
< 0.1%
6882 1
< 0.1%
6868 1
< 0.1%
6865 1
< 0.1%
6837 1
< 0.1%

Seat comfort
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8385972
Minimum0
Maximum5
Zeros4797
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:48.049549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3929832
Coefficient of variation (CV)0.49072946
Kurtosis-0.94319309
Mean2.8385972
Median Absolute Deviation (MAD)1
Skewness-0.091860998
Sum368677
Variance1.9404023
MonotonicityNot monotonic
2023-07-25T11:17:48.155312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 29183
22.5%
2 28726
22.1%
4 28398
21.9%
1 20949
16.1%
5 17827
13.7%
0 4797
 
3.7%
ValueCountFrequency (%)
0 4797
 
3.7%
1 20949
16.1%
2 28726
22.1%
3 29183
22.5%
4 28398
21.9%
5 17827
13.7%
ValueCountFrequency (%)
5 17827
13.7%
4 28398
21.9%
3 29183
22.5%
2 28726
22.1%
1 20949
16.1%
0 4797
 
3.7%

Departure/Arrival time convenient
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9906452
Minimum0
Maximum5
Zeros6664
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:48.255902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5272244
Coefficient of variation (CV)0.51066718
Kurtosis-1.089371
Mean2.9906452
Median Absolute Deviation (MAD)1
Skewness-0.25228245
Sum388425
Variance2.3324143
MonotonicityNot monotonic
2023-07-25T11:17:48.366928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 29593
22.8%
5 26817
20.6%
3 23184
17.9%
2 22794
17.6%
1 20828
16.0%
0 6664
 
5.1%
ValueCountFrequency (%)
0 6664
 
5.1%
1 20828
16.0%
2 22794
17.6%
3 23184
17.9%
4 29593
22.8%
5 26817
20.6%
ValueCountFrequency (%)
5 26817
20.6%
4 29593
22.8%
3 23184
17.9%
2 22794
17.6%
1 20828
16.0%
0 6664
 
5.1%

Food and drink
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8519941
Minimum0
Maximum5
Zeros5945
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:48.472078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4437294
Coefficient of variation (CV)0.50621751
Kurtosis-0.98672754
Mean2.8519941
Median Absolute Deviation (MAD)1
Skewness-0.11681295
Sum370417
Variance2.0843545
MonotonicityNot monotonic
2023-07-25T11:17:48.575643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28150
21.7%
4 27216
21.0%
2 27146
20.9%
1 21076
16.2%
5 20347
15.7%
0 5945
 
4.6%
ValueCountFrequency (%)
0 5945
 
4.6%
1 21076
16.2%
2 27146
20.9%
3 28150
21.7%
4 27216
21.0%
5 20347
15.7%
ValueCountFrequency (%)
5 20347
15.7%
4 27216
21.0%
3 28150
21.7%
2 27146
20.9%
1 21076
16.2%
0 5945
 
4.6%

Gate location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9904219
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:48.683281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3059699
Coefficient of variation (CV)0.4367176
Kurtosis-1.0898225
Mean2.9904219
Median Absolute Deviation (MAD)1
Skewness-0.053063895
Sum388396
Variance1.7055574
MonotonicityNot monotonic
2023-07-25T11:17:48.794526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 33546
25.8%
4 30088
23.2%
2 24518
18.9%
1 22565
17.4%
5 19161
14.8%
0 2
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 22565
17.4%
2 24518
18.9%
3 33546
25.8%
4 30088
23.2%
5 19161
14.8%
ValueCountFrequency (%)
5 19161
14.8%
4 30088
23.2%
3 33546
25.8%
2 24518
18.9%
1 22565
17.4%
0 2
 
< 0.1%

Inflight wifi service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.24913
Minimum0
Maximum5
Zeros132
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:48.904461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3188175
Coefficient of variation (CV)0.40589867
Kurtosis-1.1214461
Mean3.24913
Median Absolute Deviation (MAD)1
Skewness-0.19112285
Sum421997
Variance1.7392797
MonotonicityNot monotonic
2023-07-25T11:17:49.016956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 31560
24.3%
5 28830
22.2%
3 27602
21.3%
2 27045
20.8%
1 14711
11.3%
0 132
 
0.1%
ValueCountFrequency (%)
0 132
 
0.1%
1 14711
11.3%
2 27045
20.8%
3 27602
21.3%
4 31560
24.3%
5 28830
22.2%
ValueCountFrequency (%)
5 28830
22.2%
4 31560
24.3%
3 27602
21.3%
2 27045
20.8%
1 14711
11.3%
0 132
 
0.1%

Inflight entertainment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3834771
Minimum0
Maximum5
Zeros2978
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:49.128970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3460591
Coefficient of variation (CV)0.39783309
Kurtosis-0.53278592
Mean3.3834771
Median Absolute Deviation (MAD)1
Skewness-0.60482822
Sum439446
Variance1.8118752
MonotonicityNot monotonic
2023-07-25T11:17:49.239172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 41879
32.2%
5 29831
23.0%
3 24200
18.6%
2 19183
14.8%
1 11809
 
9.1%
0 2978
 
2.3%
ValueCountFrequency (%)
0 2978
 
2.3%
1 11809
 
9.1%
2 19183
14.8%
3 24200
18.6%
4 41879
32.2%
5 29831
23.0%
ValueCountFrequency (%)
5 29831
23.0%
4 41879
32.2%
3 24200
18.6%
2 19183
14.8%
1 11809
 
9.1%
0 2978
 
2.3%

Online support
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5197028
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:49.343506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3065107
Coefficient of variation (CV)0.37119915
Kurtosis-0.81057183
Mean3.5197028
Median Absolute Deviation (MAD)1
Skewness-0.57536498
Sum457139
Variance1.7069702
MonotonicityNot monotonic
2023-07-25T11:17:49.449552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 41510
32.0%
5 35563
27.4%
3 21609
16.6%
2 17260
13.3%
1 13937
 
10.7%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 13937
 
10.7%
2 17260
13.3%
3 21609
16.6%
4 41510
32.0%
5 35563
27.4%
ValueCountFrequency (%)
5 35563
27.4%
4 41510
32.0%
3 21609
16.6%
2 17260
13.3%
1 13937
 
10.7%
0 1
 
< 0.1%

Ease of Online booking
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.472105
Minimum0
Maximum5
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:49.552706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3055596
Coefficient of variation (CV)0.37601387
Kurtosis-0.91065426
Mean3.472105
Median Absolute Deviation (MAD)1
Skewness-0.49171965
Sum450957
Variance1.704486
MonotonicityNot monotonic
2023-07-25T11:17:49.659206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 39920
30.7%
5 34137
26.3%
3 22418
17.3%
2 19951
15.4%
1 13436
 
10.3%
0 18
 
< 0.1%
ValueCountFrequency (%)
0 18
 
< 0.1%
1 13436
 
10.3%
2 19951
15.4%
3 22418
17.3%
4 39920
30.7%
5 34137
26.3%
ValueCountFrequency (%)
5 34137
26.3%
4 39920
30.7%
3 22418
17.3%
2 19951
15.4%
1 13436
 
10.3%
0 18
 
< 0.1%

On-board service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4650755
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:49.760933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2708356
Coefficient of variation (CV)0.36675553
Kurtosis-0.78502308
Mean3.4650755
Median Absolute Deviation (MAD)1
Skewness-0.50526988
Sum450044
Variance1.6150231
MonotonicityNot monotonic
2023-07-25T11:17:49.865676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 40675
31.3%
5 31724
24.4%
3 27037
20.8%
2 17174
13.2%
1 13265
 
10.2%
0 5
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 13265
 
10.2%
2 17174
13.2%
3 27037
20.8%
4 40675
31.3%
5 31724
24.4%
ValueCountFrequency (%)
5 31724
24.4%
4 40675
31.3%
3 27037
20.8%
2 17174
13.2%
1 13265
 
10.2%
0 5
 
< 0.1%

Leg room service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4859024
Minimum0
Maximum5
Zeros444
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:49.968037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.292226
Coefficient of variation (CV)0.37070057
Kurtosis-0.84132096
Mean3.4859024
Median Absolute Deviation (MAD)1
Skewness-0.49644007
Sum452749
Variance1.669848
MonotonicityNot monotonic
2023-07-25T11:17:50.080194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 39698
30.6%
5 34385
26.5%
3 22467
17.3%
2 21745
16.7%
1 11141
 
8.6%
0 444
 
0.3%
ValueCountFrequency (%)
0 444
 
0.3%
1 11141
 
8.6%
2 21745
16.7%
3 22467
17.3%
4 39698
30.6%
5 34385
26.5%
ValueCountFrequency (%)
5 34385
26.5%
4 39698
30.6%
3 22467
17.3%
2 21745
16.7%
1 11141
 
8.6%
0 444
 
0.3%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
4
48240 
5
35748 
3
24485 
2
13432 
1
7975 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129880
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Length

2023-07-25T11:17:50.203523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T11:17:50.345327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring characters

ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 129880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Checkin service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3408069
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:50.452075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2605823
Coefficient of variation (CV)0.37732869
Kurtosis-0.79351105
Mean3.3408069
Median Absolute Deviation (MAD)1
Skewness-0.39244248
Sum433904
Variance1.5890677
MonotonicityNot monotonic
2023-07-25T11:17:50.563459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 36481
28.1%
3 35538
27.4%
5 27005
20.8%
2 15486
11.9%
1 15369
11.8%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 15369
11.8%
2 15486
11.9%
3 35538
27.4%
4 36481
28.1%
5 27005
20.8%
ValueCountFrequency (%)
5 27005
20.8%
4 36481
28.1%
3 35538
27.4%
2 15486
11.9%
1 15369
11.8%
0 1
 
< 0.1%

Cleanliness
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7057592
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:50.683094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1517739
Coefficient of variation (CV)0.31080647
Kurtosis-0.20888866
Mean3.7057592
Median Absolute Deviation (MAD)1
Skewness-0.75600069
Sum481304
Variance1.3265831
MonotonicityNot monotonic
2023-07-25T11:17:50.817147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 48795
37.6%
5 35916
27.7%
3 23984
18.5%
2 13412
 
10.3%
1 7768
 
6.0%
0 5
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 7768
 
6.0%
2 13412
 
10.3%
3 23984
18.5%
4 48795
37.6%
5 35916
27.7%
ValueCountFrequency (%)
5 35916
27.7%
4 48795
37.6%
3 23984
18.5%
2 13412
 
10.3%
1 7768
 
6.0%
0 5
 
< 0.1%

Online boarding
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.352587
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:50.960667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2987145
Coefficient of variation (CV)0.38737682
Kurtosis-0.93804992
Mean3.352587
Median Absolute Deviation (MAD)1
Skewness-0.36649561
Sum435434
Variance1.6866594
MonotonicityNot monotonic
2023-07-25T11:17:51.099392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 35181
27.1%
3 30780
23.7%
5 29973
23.1%
2 18573
14.3%
1 15359
11.8%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 15359
11.8%
2 18573
14.3%
3 30780
23.7%
4 35181
27.1%
5 29973
23.1%
ValueCountFrequency (%)
5 29973
23.1%
4 35181
27.1%
3 30780
23.7%
2 18573
14.3%
1 15359
11.8%
0 14
 
< 0.1%

Departure Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct466
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.713713
Minimum0
Maximum1592
Zeros73356
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:51.274814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile77
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.071126
Coefficient of variation (CV)2.5874589
Kurtosis100.64455
Mean14.713713
Median Absolute Deviation (MAD)0
Skewness6.8219803
Sum1911017
Variance1449.4107
MonotonicityNot monotonic
2023-07-25T11:17:51.479094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73356
56.5%
1 3682
 
2.8%
2 2855
 
2.2%
3 2535
 
2.0%
4 2309
 
1.8%
5 2136
 
1.6%
6 1884
 
1.5%
7 1748
 
1.3%
8 1618
 
1.2%
9 1552
 
1.2%
Other values (456) 36205
27.9%
ValueCountFrequency (%)
0 73356
56.5%
1 3682
 
2.8%
2 2855
 
2.2%
3 2535
 
2.0%
4 2309
 
1.8%
5 2136
 
1.6%
6 1884
 
1.5%
7 1748
 
1.3%
8 1618
 
1.2%
9 1552
 
1.2%
ValueCountFrequency (%)
1592 1
< 0.1%
1305 1
< 0.1%
1128 1
< 0.1%
1017 1
< 0.1%
978 1
< 0.1%
951 1
< 0.1%
933 1
< 0.1%
930 1
< 0.1%
921 1
< 0.1%
859 1
< 0.1%

Arrival Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct472
Distinct (%)0.4%
Missing393
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.091129
Minimum0
Maximum1584
Zeros72753
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-25T11:17:51.741476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile78
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.46565
Coefficient of variation (CV)2.5488915
Kurtosis95.117114
Mean15.091129
Median Absolute Deviation (MAD)0
Skewness6.6701246
Sum1954105
Variance1479.6062
MonotonicityNot monotonic
2023-07-25T11:17:51.949158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72753
56.0%
1 2747
 
2.1%
2 2587
 
2.0%
3 2442
 
1.9%
4 2373
 
1.8%
5 2083
 
1.6%
6 2021
 
1.6%
7 1794
 
1.4%
8 1751
 
1.3%
9 1566
 
1.2%
Other values (462) 37370
28.8%
ValueCountFrequency (%)
0 72753
56.0%
1 2747
 
2.1%
2 2587
 
2.0%
3 2442
 
1.9%
4 2373
 
1.8%
5 2083
 
1.6%
6 2021
 
1.6%
7 1794
 
1.4%
8 1751
 
1.3%
9 1566
 
1.2%
ValueCountFrequency (%)
1584 1
< 0.1%
1280 1
< 0.1%
1115 1
< 0.1%
1011 1
< 0.1%
970 1
< 0.1%
952 1
< 0.1%
940 1
< 0.1%
924 1
< 0.1%
920 1
< 0.1%
860 1
< 0.1%

Interactions

2023-07-25T11:17:42.262006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:05.746984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:08.277900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:10.927772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:13.067073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:15.299511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:17.489864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.830796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:21.980539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:24.220989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:26.584868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:28.925474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:31.057014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:33.268939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.462841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.838054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.969520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:42.403013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:05.913277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:08.513333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:11.064403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:13.199663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:15.438797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:17.624167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.961531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:22.119300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:24.354984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:26.723748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:29.057900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:31.186819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:33.407425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.597842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.973175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:40.107920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:42.544534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:06.064142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:08.666555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:11.192491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:13.337502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:15.574864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:17.761286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:20.093064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:22.255292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:24.493055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:26.864589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:29.191716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:31.319239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:33.541293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.731554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:38.106584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:40.245982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:42.678607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:06.204673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:08.813784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:11.318447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:20.217277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:22.380636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:26.994314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:29.312771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:31.441399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:33.669249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.856025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:38.228930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:40.376072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:42.814301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:06.350554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:08.966425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:11.441697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:13.587137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:15.829021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:18.011163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:20.340950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:22.508301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:24.803346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:27.130468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:29.436034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:31.595644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:33.796243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.982413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:38.353675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:40.506573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:42.948697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:09.109821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:11.562496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:13.712914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:15.953239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:18.292766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:20.464784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:33.920433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:38.473795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:40.638579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:43.080735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:06.634527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:09.258186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:11.686282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:13.837911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:16.077816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:18.416972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:20.584157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:22.763084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:29.679278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:31.850411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:34.047273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:36.234613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:38.596926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:40.768873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:43.241100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:06.769211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:25.197562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:27.673493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:29.805831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:31.975607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:34.172290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-25T11:17:34.552776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:36.895499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.084730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:41.299582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:43.785808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:07.346821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:10.090514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:12.300402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:14.499832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:16.706614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.044342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:21.200562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:23.395488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:25.773306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:28.158938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:30.292780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:32.482713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:34.685021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.021675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.206976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:41.437618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:43.918598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:07.493773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:10.229080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:12.421066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:14.628386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:16.830957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.166456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:21.324675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:23.522261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:25.906580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:28.279755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:30.415920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:32.607749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:34.807390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.147427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.329340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:41.570972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:44.055862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:07.654411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:10.364390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:12.543708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:14.758047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:16.957028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.290390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:21.446196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:23.651862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:26.036905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:28.403164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:30.537481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:32.733611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:34.935873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.269268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.454484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:41.706244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:44.190477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:07.797456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:10.500170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:12.665566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:14.885697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:17.080444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.410052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:21.572753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:23.777203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:26.164389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:28.524862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:30.661921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:32.859702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.059682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.396664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.574144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:41.838831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:44.326512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:07.968056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:10.632086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:12.792802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:15.013175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:17.209379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.531445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:21.696695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:23.936708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:26.294623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:28.650413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:30.783288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:32.985758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.184611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.556378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.697918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:41.971686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:44.469639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:08.120771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:10.781570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:12.928525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:15.155905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:17.348318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:19.668930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:21.838774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:24.077252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:26.438437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:28.786952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:30.920009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:33.126781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:35.323552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:37.698541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:39.834968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T11:17:42.116348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-25T11:17:52.151843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeFlight DistanceSeat comfortDeparture/Arrival time convenientFood and drinkGate locationInflight wifi serviceInflight entertainmentOnline supportEase of Online bookingOn-board serviceLeg room serviceCheckin serviceCleanlinessOnline boardingDeparture Delay in MinutesArrival Delay in MinutessatisfactionGenderCustomer TypeType of TravelClassBaggage handling
Age1.000-0.2480.0090.0380.015-0.0010.0130.1340.1230.0760.0780.0930.036-0.0040.039-0.009-0.0110.2040.0150.3780.3420.2080.045
Flight Distance-0.2481.000-0.0470.001-0.011-0.0040.005-0.037-0.035-0.026-0.029-0.0290.0040.0100.0040.0560.0390.2000.1770.2570.1590.2150.032
Seat comfort0.009-0.0471.0000.4390.7050.4100.1290.3990.1200.2020.1180.1240.0440.1060.131-0.031-0.0390.4700.1210.0740.0580.0570.131
Departure/Arrival time convenient0.0380.0010.4391.0000.5380.555-0.0040.063-0.003-0.0040.0590.0220.0660.077-0.003-0.005-0.0070.0400.0630.2880.2110.0670.061
Food and drink0.015-0.0110.7050.5381.0000.5340.0230.3220.0250.0350.0370.0610.0120.0330.012-0.013-0.0160.2650.0900.0890.0830.0610.042
Gate location-0.001-0.0040.4100.5550.5341.000-0.004-0.0040.0020.000-0.024-0.007-0.032-0.010-0.0030.0050.0060.1430.0380.1460.0780.0820.051
Inflight wifi service0.0130.0050.129-0.0040.023-0.0041.0000.2600.5340.5800.0590.0310.0880.0490.617-0.022-0.0320.2450.0360.0970.0300.0610.043
Inflight entertainment0.134-0.0370.3990.0630.322-0.0040.2601.0000.4610.3400.2060.1750.2370.1470.370-0.035-0.0510.6400.1540.2480.0920.1820.104
Online support0.123-0.0350.120-0.0030.0250.0020.5340.4611.0000.6040.1740.1510.2110.1270.650-0.022-0.0380.4330.0940.2000.0660.1390.093
Ease of Online booking0.076-0.0260.202-0.0040.0350.0000.5800.3400.6041.0000.4650.3770.1370.4520.663-0.032-0.0470.4540.0860.1620.0440.1010.375
On-board service0.078-0.0290.1180.0590.037-0.0240.0590.2060.1740.4651.0000.4260.2380.5790.139-0.030-0.0490.3610.0650.1040.0520.1240.415
Leg room service0.093-0.0290.1240.0220.061-0.0070.0310.1750.1510.3770.4261.0000.1560.4230.110-0.010-0.0250.3360.0980.1250.0700.1040.312
Checkin service0.0360.0040.0440.0660.012-0.0320.0880.2370.2110.1370.2380.1561.0000.2500.177-0.019-0.0350.2810.0180.0460.0610.1130.144
Cleanliness-0.0040.0100.1060.0770.033-0.0100.0490.1470.1270.4520.5790.4230.2501.0000.118-0.038-0.0590.3050.0320.0530.0680.1030.500
Online boarding0.0390.0040.131-0.0030.012-0.0030.6170.3700.6500.6630.1390.1100.1770.1181.000-0.020-0.0340.3500.0590.1290.0330.0900.073
Departure Delay in Minutes-0.0090.056-0.031-0.005-0.0130.005-0.022-0.035-0.022-0.032-0.030-0.010-0.019-0.038-0.0201.0000.7400.0440.0020.0000.0040.0000.005
Arrival Delay in Minutes-0.0110.039-0.039-0.007-0.0160.006-0.032-0.051-0.038-0.047-0.049-0.025-0.035-0.059-0.0340.7401.0000.0450.0000.0000.0000.0000.006
satisfaction0.2040.2000.4700.0400.2650.1430.2450.6400.4330.4540.3610.3360.2810.3050.3500.0440.0451.0000.2120.2930.1090.3120.310
Gender0.0150.1770.1210.0630.0900.0380.0360.1540.0940.0860.0650.0980.0180.0320.0590.0020.0000.2121.0000.0310.0090.0120.038
Customer Type0.3780.2570.0740.2880.0890.1460.0970.2480.2000.1620.1040.1250.0460.0530.1290.0000.0000.2930.0311.0000.3080.1230.064
Type of Travel0.3420.1590.0580.2110.0830.0780.0300.0920.0660.0440.0520.0700.0610.0680.0330.0040.0000.1090.0090.3081.0000.5540.061
Class0.2080.2150.0570.0670.0610.0820.0610.1820.1390.1010.1240.1040.1130.1030.0900.0000.0000.3120.0120.1230.5541.0000.107
Baggage handling0.0450.0320.1310.0610.0420.0510.0430.1040.0930.3750.4150.3120.1440.5000.0730.0050.0060.3100.0380.0640.0610.1071.000

Missing values

2023-07-25T11:17:44.768343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-25T11:17:45.551133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

satisfactionGenderCustomer TypeAgeType of TravelClassFlight DistanceSeat comfortDeparture/Arrival time convenientFood and drinkGate locationInflight wifi serviceInflight entertainmentOnline supportEase of Online bookingOn-board serviceLeg room serviceBaggage handlingCheckin serviceCleanlinessOnline boardingDeparture Delay in MinutesArrival Delay in Minutes
0satisfiedFemaleLoyal Customer65Personal TravelEco2650002242330353200.0
1satisfiedMaleLoyal Customer47Personal TravelBusiness246400030223444232310305.0
2satisfiedFemaleLoyal Customer15Personal TravelEco21380003202233444200.0
3satisfiedFemaleLoyal Customer60Personal TravelEco6230003343110141300.0
4satisfiedFemaleLoyal Customer70Personal TravelEco3540003434220242500.0
5satisfiedMaleLoyal Customer30Personal TravelEco18940003202254554200.0
6satisfiedFemaleLoyal Customer66Personal TravelEco227000325555055531715.0
7satisfiedMaleLoyal Customer10Personal TravelEco18120003202233454200.0
8satisfiedFemaleLoyal Customer56Personal TravelBusiness730003535440154400.0
9satisfiedMaleLoyal Customer22Personal TravelEco1556000320222453423026.0
satisfactionGenderCustomer TypeAgeType of TravelClassFlight DistanceSeat comfortDeparture/Arrival time convenientFood and drinkGate locationInflight wifi serviceInflight entertainmentOnline supportEase of Online bookingOn-board serviceLeg room serviceBaggage handlingCheckin serviceCleanlinessOnline boardingDeparture Delay in MinutesArrival Delay in Minutes
129870satisfiedFemaledisloyal Customer70Personal TravelEco1674545155553245455446.0
129871satisfiedFemaledisloyal Customer35Personal TravelEco32875453252245443290.0
129872satisfiedFemaledisloyal Customer69Personal TravelEco22405453454454434440.0
129873satisfiedFemaledisloyal Customer63Personal TravelEco1942554434335253537NaN
129874satisfiedFemaledisloyal Customer11Personal TravelEco27525552252235354250.0
129875satisfiedFemaledisloyal Customer29Personal TravelEco17315553252233444200.0
129876dissatisfiedMaledisloyal Customer63Personal TravelBusiness208723242113233121174172.0
129877dissatisfiedMaledisloyal Customer69Personal TravelEco232030333224434232155163.0
129878dissatisfiedMaledisloyal Customer66Personal TravelEco245032323223323212193205.0
129879dissatisfiedFemaledisloyal Customer38Personal TravelEco430734333334555333185186.0